An HMM-based threshold model approach for gesture recognition

The task of automatic gesture recognition is highly challenging due to the presence of unpredictable and ambiguous nongesture hand motions. In this paper, a new method is developed using the Hidden Markov Model based technique. To handle nongesture patterns, we introduce the concept of a threshold model that calculates the likelihood threshold of an input pattern and provides a confirmation mechanism for the provisionally matched gesture patterns. The threshold model is a weak model for all trained gestures in the sense that its likelihood is smaller than that of the dedicated gesture model for a given gesture. Consequently, the likelihood can be used as an adaptive threshold for selecting proper gesture model. It has, however, a large number of states and needs to be reduced because the threshold model is constructed by collecting the states of all gesture models in the system. To overcome this problem, the states with similar probability distributions are merged, utilizing the relative entropy measure. Experimental results show that the proposed method can successfully extract trained gestures from continuous hand motion with 93.14 percent reliability.